19,045 research outputs found
Crime and Social media
Purpose-The study complements the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012.
Design/methodology/approach-The empirical evidence is based on Ordinary Least Squares (OLS), Tobit and Quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the dataset is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa, Sub-Saharan Africa and Latin America.
Findings-From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, Quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime decile. Further, when the dataset is decomposed into regions and income levels, the negative relationship is evident in the Middle East and North Africa (MENA) while a positive relationship is confirmed for sub-Saharan Africa. Policy implications are discussed.
Originality/value- Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available dataset on Facebook
Exploratory Analysis of Pairwise Interactions in Online Social Networks
In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4
on pages 422 to 42
How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL
Social relationships are a natural basis on which humans make trust
decisions. Online Social Networks (OSNs) are increasingly often used to let
users base trust decisions on the existence and the strength of social
relationships. While most OSNs allow users to discover the length of the social
path to other users, they do so in a centralized way, thus requiring them to
rely on the service provider and reveal their interest in each other. This
paper presents Social PaL, a system supporting the privacy-preserving discovery
of arbitrary-length social paths between any two social network users. We
overcome the bootstrapping problem encountered in all related prior work,
demonstrating that Social PaL allows its users to find all paths of length two
and to discover a significant fraction of longer paths, even when only a small
fraction of OSN users is in the Social PaL system - e.g., discovering 70% of
all paths with only 40% of the users. We implement Social PaL using a scalable
server-side architecture and a modular Android client library, allowing
developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This
is the full versio
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Regression adjustments for estimating the global treatment effect in experiments with interference
Standard estimators of the global average treatment effect can be biased in
the presence of interference. This paper proposes regression adjustment
estimators for removing bias due to interference in Bernoulli randomized
experiments. We use a fitted model to predict the counterfactual outcomes of
global control and global treatment. Our work differs from standard regression
adjustments in that the adjustment variables are constructed from functions of
the treatment assignment vector, and that we allow the researcher to use a
collection of any functions correlated with the response, turning the problem
of detecting interference into a feature engineering problem. We characterize
the distribution of the proposed estimator in a linear model setting and
connect the results to the standard theory of regression adjustments under
SUTVA. We then propose an estimator that allows for flexible machine learning
estimators to be used for fitting a nonlinear interference functional form. We
propose conducting statistical inference via bootstrap and resampling methods,
which allow us to sidestep the complicated dependences implied by interference
and instead rely on empirical covariance structures. Such variance estimation
relies on an exogeneity assumption akin to the standard unconfoundedness
assumption invoked in observational studies. In simulation experiments, our
methods are better at debiasing estimates than existing inverse propensity
weighted estimators based on neighborhood exposure modeling. We use our method
to reanalyze an experiment concerning weather insurance adoption conducted on a
collection of villages in rural China.Comment: 38 pages, 7 figure
The power of A/B testing under interference
In this paper, we address the fundamental statistical question: how can you
assess the power of an A/B test when the units in the study are exposed to
interference? This question is germane to many scientific and industrial
practitioners that rely on A/B testing in environments where control over
interference is limited. We begin by proving that interference has a measurable
effect on its sensitivity, or power. We quantify the power of an A/B test of
equality of means as a function of the number of exposed individuals under any
interference mechanism. We further derive a central limit theorem for the
number of exposed individuals under a simple Bernoulli switching interference
mechanism. Based on these results, we develop a strategy to estimate the power
of an A/B test when actors experience interference according to an observed
network model. We demonstrate how to leverage this theory to estimate the power
of an A/B test on units sharing any network relationship, and highlight the
utility of our method on two applications - a Facebook friendship network as
well as a large Twitter follower network. These results yield, for the first
time, the capacity to understand how to design an A/B test to detect, with a
specified confidence, a fixed measurable treatment effect when the A/B test is
conducted under interference driven by networks.Comment: 14 page
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